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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
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@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. Health Nerd‏Verified account @GidMK 24 Mar 2020

      Health Nerd Retweeted

      So I've seen these numbers thrown around a lot I think this analysis is pretty flawed and very likely to be wrong Some reasons #COVID19 https://twitter.com/ToryFibs/status/1242561095761637376 …

      Health Nerd added,

      This Tweet is unavailable.
      3 replies 7 retweets 20 likes
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    2. Health Nerd‏Verified account @GidMK 24 Mar 2020

      What did the authors do? They fitted a fairly standard model of epidemics (SIR = Susceptible, Infected, Recovered) looking at the number of infections and deaths in the UK to see how many cases there might be in the populationpic.twitter.com/mqDdDvHQUP

      1 reply 0 retweets 2 likes
      Show this thread
    3. Health Nerd‏Verified account @GidMK 24 Mar 2020

      However, there was a key assumption in the model that makes it problematic The scientists assumed that only ~1% of people who caught #COVID19 would be hospitalizedpic.twitter.com/VNgtbjFzDr

      1 reply 0 retweets 5 likes
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    4. Health Nerd‏Verified account @GidMK 24 Mar 2020

      Is this assumption reasonable? In my opinion, no. Even the sources cited in this paper don't agree with their key assumption, like WHO joint report into COVID-19 in Chinapic.twitter.com/BS07T4omP0

      1 reply 0 retweets 4 likes
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    5. Health Nerd‏Verified account @GidMK 24 Mar 2020

      If you assume that only 1% of people get hospitalized due to #COVID19, the models follow pretty simply I.e. if 10,000 people are hospitalized, 1 million people would have the disease

      1 reply 0 retweets 3 likes
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    6. Health Nerd‏Verified account @GidMK 24 Mar 2020

      And despite the fancy language, that's essentially what the authors did. The SIR model they produced is mostly a function of this one assumption

      1 reply 0 retweets 4 likes
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    7. Health Nerd‏Verified account @GidMK 24 Mar 2020

      Now, the authors actually had a good reason for this. They are arguing that it is POSSIBLE that this is true, and we need more testing to ensure that it is not the casepic.twitter.com/hnHwT37Zlg

      1 reply 0 retweets 3 likes
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    8. Health Nerd‏Verified account @GidMK 24 Mar 2020

      However, that is not the message that the media - and most of twitter - have picked up onpic.twitter.com/gsTAdqDlQ3

      1 reply 0 retweets 2 likes
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    9. Health Nerd‏Verified account @GidMK 24 Mar 2020

      Based on all published evidence to date, it is INCREDIBLY UNLIKELY that the hospitalization rate is only 1% for #COVID19 infections The true rate appears to be quite a bit higherpic.twitter.com/pygB0sP5ay

      1 reply 4 retweets 9 likes
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      Health Nerd‏Verified account @GidMK 24 Mar 2020

      What all this noise really shows is how one small assumption can completely change the predicted results of a model #COVID19

      5:32 PM - 24 Mar 2020
      • 3 Retweets
      • 21 Likes
      • Dania Orta-Alemán Dr Pallave Dasari 🐘🐨🦀 Bruno Meekings Nathan Brouwer LlamaOfAusangate George Beckingham Dr. Jenny M Groarke Dr Ruth Ann Harpur Rob England 🦓
      3 replies 3 retweets 21 likes
        1. Robin‏ @VesterGirl 24 Mar 2020
          Replying to @GidMK

          TY once again for the careful reasoning! Whenever I see a new report or headline, I look to see if other experts have reviewed and responded - very helpful. (though a 1% hosp rate sounds like such a dream compared to other data rolling in).

          0 replies 0 retweets 0 likes
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        1. Dr. Jenny M Groarke‏ @JennyMGroarke 24 Mar 2020
          Replying to @GidMK

          @Paul_Toner for the win

          0 replies 0 retweets 2 likes
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        1. New conversation
        2. Nathan Brouwer‏ @lobrowR 24 Mar 2020
          Replying to @GidMK

          Why don’t people do Monte Carlo simulations to add uncertainty to these things? Slap a distribution on all assumptions. Reviewers at all stages should demand it

          1 reply 0 retweets 1 like
        3. Torbjørn Wisløff‏ @TorbWis 25 Mar 2020
          Replying to @lobrowR @GidMK

          No, reviewers should abolutely not demand it! Reviewers should stick to their task and not suggest every expansion to manuscripts that they could think of. I fully agree that it should be done more, also on infectious disease models, but this is more complex than it may seem.

          0 replies 0 retweets 0 likes
        4. End of conversation

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